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GOOGLE EARTH ENGINE
INTRODUCTION
Google Earth Engine,
Such applications may include:
1. Mapping and predicting disease outbreaks.
2. Natural resource management
3. Flood risk mapping
4. Urban growth mapping
5. Crop health monitoring
The list is endless. It all depends on your ingenuity and open mindedness.
To be able to carry out your remote sensing research or projects using GEE, one must leverage the Earth Engine API that primarily uses JavaScript as the programming language.
However, Geemap, a python package for interactive geospatial analysis and visualization with GEE has been created to give persons more comfortable with Python to leverage GEE with ease.
EARTH ENGINE BASICS
As previously stated, GEE uses its API that runs on JavaScript. So before diving into geospatial analysis with GEE, we must first start with the basics.
The simple 'hello world' statement
//This prints the string below onto the console.
print("Hello World");
//Variables
Just like in other programming languages you may have come across; JavaScript also has variables.
Variables are simply named memory locations a user defines in a program. The “var” keyword is used.
var city = 'Nairobi';
var country = 'Kenya';
print(city, country);
var population = 8400000;
print(population);
// List
A collection of items stored within square braces
var majorCities = ['Mumbai', 'Delhi', 'Chennai', 'Kolkata'];
print(majorCities);
// Dictionary
A collection of items stored within curly braces such that for every key, there exists a corresponding value
var cityData = {
city: “city”,
'population': 8400000,
};
print(cityData);
// Function
A block of code which when called, carries out a particular task
var greet = function(name) {
return 'Hello ' + name;
};
print(greet('World'));
WORKING WITH IMAGE COLLECTIONS
As previously stated, GEE provides a vast collection of datasets. These datasets are found on the datasets page on the GEE platform. Likewise, you can also try looking up your datasets using the search bar at the code editor.
So, upon getting your dataset, we copy the code snippet provided
//LOADING LANDSAT9 IMAGERY
var landsat9 = ee.ImageCollection("LANDSAT/LC09/C02/T1_L2").select('SR_B1','SR_B2','SR_B3','SR_B4','SR_B5','SR_B6','SR_B7');
The .select() feature allows you to select the bands you want your image to possess.
//LOADING MY FEATURE COLLECTION
var counties = ee.FeatureCollection("projects/ee-chrysanthusjumaa23/assets/counties");
//FILTERING MY FEATURE COLLECTION TO OBTAIN MY AOI
var Kericho_extract = counties.filter(ee.Filter.eq('COUNTY_NAM','KERICHO'));
//CONVERTING MY CLIPPED FEATURE TO GEOMETRY
var geometry = Kericho_extract.geometry();
//FILTERING MY IMAGE COLLECTION FOR CLOUD COVER
var filter1 = landsat9.filter(ee.Filter.lt('CLOUD_COVER',20));
//FILTERING DATES
var filter2 = filter1.filter(ee.Filter.date('2024-01-01','2024-01-31'));
//FILTERING BOUNDS TO AOI
var filter3 = filter2.filter(ee.Filter.bounds(geometry));
//CHECKING NUMBER OF IMAGES MATCHING FILTERS
print(filter3.size());
//CENTERING IMAGE TO VIEWPORT
Map.centerObject(geometry,7.9);
We have two kinds of composite; the median composite and the mosaic composite.
//CREATING MEDIAN COMPOSITE
var mediancomposite = filter3.median();
//CREATING MOSAIC COMPOSITE
var UNclipped_Kericho3MOSAIC = filter3.mosaic();
//CREATING ORIGINAL AOI CLIPPED COMPOSITE
//var clipped_Kericho1 = filter3.clip(geometry);
//CREATING MEDIAN CLIPPED COMPOSITE
var clipped_Kericho2 = mediancomposite.clip(geometry);
//CREATING MOSAIC CLIPPED COMPOSITE
var clipped_Kericho3 = UNclipped_Kericho3MOSAIC.clip(geometry);
//CREATING VISUALISATION PARAMETERS
var visparams = {
min: 0.0,
max: 30000.0,
bands: ['SR_B4','SR_B3','SR_B2']
};
//ADDING THE IMAGES TO VIEWPORT
// Map.addLayer(filter3,visparams,'AOI COMPOSITE');
// Map.addLayer(mediancomposite,visparams,'mediancomposite');
//Map.addLayer(UNclipped_Kericho3MOSAIC,visparams,'mosaiccomposite');
//Map.addLayer(clipped_Kericho1,visparams,'clipped_Kericho1');
//Map.addLayer(clipped_Kericho2,visparams,'clipped_Kericho2');
Map.addLayer(clipped_Kericho3,visparams,'clipped_Kericho3');
//EXPORTING IMAGE
Export.image.toDrive({
image: clipped_Kericho3,
description: 'KERICHO_COUNTY_EXPORT',
folder: 'PRIME REMOTE SENSING PRACTICAL IMAGES',
fileNamePrefix: 'Corrected kericho in JANUARY',
region: geometry,
scale: 30,
crs: 'EPSG:4326',
maxPixels: 1e10,
});